Mission

Why Ampliova exists.

The gap between a raw C_qvalue and a defensible fold change shouldn’t take an afternoon, three spreadsheets, and a quiet prayer to the housekeeping gene. Ampliova automates the analytical chain that rigorous qPCR requires: efficiency correction, geometric mean normalization, and outlier detection. All reproducible by default.

Built for PhD students, postdocs, and principal investigators in molecular biology who need defensible, traceable results without building their own analysis pipeline.

Methodology

Built on sound methodology.

Two quantification methods.Efficiency-corrected relative quantification derives per-reaction efficiencies directly from raw amplification curves using LinRegPCR (Ruijter et al.), then applies E^−Cqper gene. Classic ΔΔCq (Livak 2001) assumes E = 2 throughout. Both paths share the same normalization, outlier detection, and export pipeline.

Reference gene selection. Before running an analysis, Ampliova evaluates candidate housekeeping genes using three independent stability algorithms: geNorm (Vandesompele 2002), NormFinder (Andersen 2004), and BestKeeper (Pfaffl 2004). Their ranks are combined into a RefFinder composite score. Expression is then normalized to the geometric mean of the selected reference genes, in line with MIQE 2.0 guidelines.

Multi-plate experiments. An inter-run calibrator (IRC) sample present on every plate enables cross-plate normalization without rerunning all samples together. Plates can therefore span different genes or different sample cohorts and still yield comparable fold changes.

Traceability. Every intermediate value is preserved: per-well efficiency, R_q, normalized quantity, calibrator mean, mean C_q. All accessible in the table view or exported to XLSX. No black-box corrections. Every decision follows published, peer-reviewed criteria and is reported verbatim in the auto-generated Methods section.

Amplification curve · log₁₀ scale

Log₁₀ fluorescencePCR cycles

LinRegPCR works on the log₁₀-transformed fluorescence signal. In this scale, the exponential phase appears as a straight line (window of linearity, amber). A linear regression over this region gives the slope, from which per-reaction efficiency E is derived: E = 10^slope.

Raw curves

LinRegPCR

E per well

Mean E / gene

Normalize

Fold change

Full methodology and formulas → /docs

Scientific credits

References

Disclosure. Reference 1 below is co-authored by Louis Müller, Ampliova’s founder. It is a peer-reviewed, independently refereed protocol (Bio-Protocol), and the direct methodological basis for this product — not an independent endorsement of it.

Müller, L. and Tiret, L. (2026). Efficiency-Corrected Relative Quantification of qPCR Data Using LinRegPCR and a Spreadsheet-Based Workflow. Bio-Protocol. doi:10.21769/BioProtoc.5729
Untergasser, A., Ruijter, J. M., Benes, V. and Van Den Hoff, M. J. B. (2021). Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data. BMC Bioinformatics, 22(1), 398.
Bustin, S. A., et al. (2025). MIQE 2.0: Revision of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines. Clin. Chem., 71(6), 634–651.
Livak, K. J. and Schmittgen, T. D. (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2^−ΔΔC_q Method. Methods, 25(4), 402–408.
Vandesompele, J., et al. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol., 3(7), research0034.1.
Andersen, C. L., Jensen, J. L. and Ørntoft, T. F. (2004). Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach. Cancer Res., 64(15), 5245–5250.
Pfaffl, M. W., Tichopad, A., Prgomet, C. and Neuvians, T. P. (2004). Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper, Excel-based tool using pair-wise correlations. Biotechnol. Lett., 26(6), 509–515.